Method for determining the step size for an LMS adaptive equalizer for 8VSB

ABSTRACT

A method and system for determining a step size of an adaptive equalizer for a digital data receiver. The data received by the receiver includes coded symbols and uncoded symbols. The method includes determining a first error estimate based on decoded symbols corresponding to the coded symbols, determining a second error estimate based on the uncoded symbols, adaptively selecting the first error estimate or the second error estimate based on a convergence criterion, and determining a step size based on the selected error estimate.

RELATED APPLICATION

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 60/885,692, filed on Jan. 19, 2007, the entire contents ofwhich are incorporated herein by reference.

FIELD

Embodiments of the invention relate generally to digital communicationsystems and methods, and particularly to digital television receivers.

BACKGROUND

In 1996, the Advanced Television Systems Committee, Inc. (“ATSC”)adopted an ATSC digital television (“DTV”) terrestrial transmissionstandard. Several generations of receivers have been developed sinceadoption of the ATSC DTV standard. Generally, each generation ofreceivers was developed to improve reception performance over previousgenerations of receivers. A main impediment to good reception is severemultipath interference. Hence, complicated equalizers were developed forreceivers in order to improve receiver performance by mitigating theeffects of the multipath interference.

Terrestrial broadcast DTV channel presents quite a difficult multipathenvironment. Relatively strong duplicates of the transmitted signal mayarrive at a receiver via various reflected signal paths as well as viathe direct path from transmitter to receiver. In some cases, there is nodirect path from transmitter to receiver, and all received signal pathsare via reflection. If the path carrying the strongest signal isregarded as the main signal path, reflected signals may arrive at thereceiver both prior to or subsequent to the main signal. The arrivaltime differences among various signal paths, compared to that of themain signal path, can be large. Also, these reflected signals may varyin time, both in terms of amplitude and delay relative to the mainsignal path.

During a typical transmission, data is transmitted in frames 100 asshown in FIG. 1. Each frame 100 is composed of two fields 104, 108. Eachof the fields 104, 108 includes 313 segments. Each of the segmentsincludes 832 symbols. As such, each of the fields 104, 108 includes atotal of 260,416 symbols. Each of the segments begins with a four-symbolsequence, referred to as a segment sync, which comprises four symbols[+5, −5, −5, +5]. The first segment in each field is a field syncsegment.

FIG. 2 shows an exemplary field sync segment 200 of the field 104 or 108of FIG. 1. The field sync segment 200 includes a segment sync 204, apseudo noise sequence 208 that comprises 511 symbols (PN511), a pseudonoise sequence 212 that comprises 63 symbols (PN63), a second PN63sequence 216, and a third PN63 sequence 220. The third PN63 sequence 220is followed by a mode sequence 224 that comprises 24 symbols to indicatea transmitting mode of 8-level vestigial sideband (“8VSB”). In alternatefields, the three PN63 sequences 212, 216, 220 are the same. In theremaining fields, the first and third PN63 sequences 212, 220 are thesame while the second PN63 sequence 216 is inverted. In either case, thefirst 728 symbols of the field sync segment 200 are a priori known to areceiver and may be used for equalizer training. The mode sequence 224is followed by a reserved mode sequence 228 of 92 symbols composingvarious mode and reserved fields that are not a priori known to thereceiver. The sequences 204, 208, 212, 216, 220, 224, and 228 symbolsuse a symbol set of {+5, −5}. The field sync segment 200 ends with aprecode sequence 232 comprising 12 symbols that use a symbol set of {−7,−5, −3, −1, +1, +3, +5, +7}, and are duplicates of the last 12 symbolsof the preceding data field. These are thus called precode symbols.

The remaining 312 segments of each field 104, 108 are referred to asdata segments. An exemplary data segment 300 is shown in FIG. 3. Afterthe segment sync symbols 204, the data segment 300 includes a datasequence 304 that comprises 828 symbols. The symbols are trellis encodedby a 12 phase trellis encoder that results in 8-level symbols from asymbol set of {−7, −5, −3, −1, +1, +3, +5, +7}.

FIG. 4 shows a digital data (e.g., 8VSB) transmitter 400. Thetransmitter 400 includes a randomizer 404 that randomizes data to betransmitted, a Reed-Solomon encoder 408 that encodes the randomized datafrom the randomizer 404, and an interleaver 412 that interleavesReed-Solomon byte-wise encoded data. The transmitter 400 also includes atrellis encoder 416 that encodes the interleaved data. An exemplarytrellis encoder 416 is a 12-phase trellis encoder. A data frameformatter 420 subsequently adds segment sync symbols and field syncsymbols to the trellis coded data at appropriate times to create a dataframe structure like that of FIG. 1. A pilot insertion module 424 theninserts a pilot carrier frequency signal by adding a fixed DC level toeach of the symbols.

A modulator 428 then implements root raised cosine pulse shaping andmodulates the signal for RF transmission as an 8VSB signal at a symbolrate of 10.76 MHz. The 8VSB signal differs from other commonly usedlinear modulation methods such as quadrature amplitude modulation(“QAM”) in that the 8VSB symbols are real, but have a pulse shape thatis complex with only the real part of the pulse having a Nyquist shape.

FIG. 5 shows a block diagram of a digital data (e.g., 8VSB) receiver500. The receiver 500 includes a tuner 504 to receive RF signalstransmitted from the transmitter 400, and a demodulator 508 todemodulate the RF signal to baseband. The receiver 500 also includes async and timing recovery module 512 to perform symbol clock timing andframe synchronization recovery on the demodulated signals. The receiver500 also includes a matched filter 516 to filter the recovered signals,an equalizer 520 that equalizes the filtered signals, a phase tracker524 that reduces the phase noise of the equalized signals, a trellisdecoder 528 that decodes the noise-reduced equalized signals, adeinterleaver 532 that deinterleaves the decoded signals, a Reed-Solomondecoder 536 that decodes the deinterleaved signals, and a derandomizer540 that derandomizes the decoded signals.

The multipath RF channel between the transmitter 400 and the receiver500 can be viewed in its baseband equivalent form. For example, thetransmitted signal has a root raised cosine spectrum with a nominalbandwidth of 5.38 MHz and an excess bandwidth of 11.5% centered at onefourth of the symbol rate (i.e., 2.69 MHz). Thus, the transmitted pulseshape or pulse q(t) is complex and given by EQN. (1):q(t)=e ^(jπF) ^(s) ^(t/2) q _(RRC)(t)  (1)where F_(s) is a symbol frequency, and q_(RRC)(t) is a real square rootraised cosine pulse with an excess bandwidth of 11.5% of the multipathRF channel. The pulse q(t) is referred to as a “complex root raisedcosine pulse.” For an 8VSB system, the transmitted pulse shape q(t) andthe received and matched filter pulse shape q*(−t) are identical sinceq(t) is conjugate-symmetric. Thus, the raised cosine pulse p(t),referred to as the “complex raised cosine pulse,” is given by EQN. (2):p(t)=q(t)*q*(−t)  (2)where * denotes convolution, and * denotes complex conjugation.

The transmitted baseband signal with a data rate of 1/T symbols/sec canbe represented by EQN. (3):

$\begin{matrix}{{{s(t)} = {\sum\limits_{k}^{\;}\;{I_{k}{q\left( {t - {kT}} \right)}}}},} & (3)\end{matrix}$where {I_(k)εA≡{α₁, . . . α₈}⊂R¹} is a transmitted data sequence, whichis a discrete 8-ary sequence taking values of the real 8-ary alphabet A.For 8VSB, the alphabet set is {−7, −5, −3, −1, +1, +3, +5, +7}.

A physical channel between the transmitter 400 and the receiver 500 isdenoted c(t) and can be described by

$\begin{matrix}{{c(t)} = {\sum\limits_{k = {- L_{ha}}}^{L_{hc}}\;{c_{k}{\delta\left( {t - \tau_{k}} \right)}}}} & (4)\end{matrix}$where {c_(k)(τ)}⊂C¹, and L_(ha) and L_(hc) are the maximum number ofanti-causal and causal multipath delays, respectively. Constant τ_(k) isa multipath delay, and variable δ(t) is a Dirac delta function. Hence,the overall channel impulse response is given by EQN. (5):

$\begin{matrix}{{h(t)} = {{{p(t)}*{c(t)}} = {\sum\limits_{- L_{ha}}^{L_{hc}}\;{c_{k}{p\left( {t - \tau_{k}} \right)}}}}} & (5)\end{matrix}$

The matched filter output y(t) in the receiver prior to equalization isgiven by EQN. (6):

$\begin{matrix}{{{y(t)} = {{\left( {\sum\limits_{k}^{\;}\;{\delta\left( {t - {kT}} \right)}} \right)*{h(t)}} + {v(t)}}},} & (6)\end{matrix}$where v(t) is given by EQN. (7):v(t)=η(t)*q*(−t)  (7)which denotes a complex or colored noise process after the pulse matchedfilter, with η(t) being a zero-mean white Gaussian noise process withspectral density σ_(n) ² per real and imaginary part. Sampling thematched filter output y(t) at the symbol rate produces a discrete timebaseband representation of the input to the equalizer 520, as shown inEQN. (8):

$\begin{matrix}{{{{y\lbrack n\rbrack} \equiv {y(t)}}❘_{t = {nT}}} = {{\sum\limits_{k}^{\;}\;{I_{k}{h\left\lbrack {n - k} \right\rbrack}}} + {{v\lbrack n\rbrack}.}}} & (8)\end{matrix}$

As stated above, for each data field of 260,416 symbols, only 728symbols, which reside in the field sync segment 200, are a priori knownand thus available for equalizer training. Furthermore, conditions ofthe multipath channel are generally not known a priori. As such, theequalizer 520 in the receiver 500 is so configured to adaptivelyidentify and combat various multipath channel conditions.

In the following discussion, n represents a sample time index, regulartype represents scalar variables, bold lower case type represents vectorvariables, bold upper case type represents matrix variables, a *superscript indicates complex conjugation, and the ^(H) superscriptindicates conjugate transposition (Hermitian).

The equalizer 520 may be implemented as, or employ equalizationtechniques relating to, linear equalizers (“LEs”), decision feedbackequalizers (“DFEs”), and predictive decision feedback equalizers(“pDFEs”). Equalizer tap weight adaptation is often achieved via a leastmean square (“LMS”) algorithm or system, which is a low complexitymethod for adaptively approximating a minimum mean squared error(“MMSE”) tap weight solution, or equivalently a solution to the WeinerHopf equations, described below.

In the case of an LE, let u[n] be an N long equalizer input vector, y[n]be the equalizer output w^(H)[n]u[n], where w^(H)[n] is an N longequalizer tap weight vector of a linear transversal filter or anadaptive filter,

R_(uu)[n]=E(u[n]u^(H)[n]) has a size of N×N, and

r_(du)=E(u[n]d*[n])

Then e[n] d[n]−y[n] where d[n] is the desired symbol.

The mean squared error (“MSE”) is given by J=E(e[n]e*[n]). It can beshown that the MSE as a function of filter taps w, J(w), is given by (nindex omitted for clarity) EQN. (9):J(w)=σ_(d) ² −w ^(H) r _(du) −r _(du) ^(H) w+w ^(H) R ^(uu) w  (9)A gradient vector of J(w) is given by EQN. (10):

$\begin{matrix}{{\nabla{J(w)}} = {{2\frac{\partial{J(w)}}{\partial w^{*}}} = {\begin{bmatrix}{\frac{\partial J}{\partial w_{0}^{R}} + {j\frac{\partial J}{\partial w_{0}^{I}}}} \\{\frac{\partial J}{\partial w_{1}^{R}} + {j\frac{\partial J}{\partial w_{1}^{I}}}} \\ - \\ - \\{\frac{\partial J}{\partial w_{N - 1}^{R}} + {j\frac{\partial J}{\partial w_{N - 1}^{I}}}}\end{bmatrix} = {2\left( {{R_{uu}w} - r_{du}} \right)}}}} & (10)\end{matrix}$

An optimal MMSE tap vector w_(opt) is found by setting ∇J(w)=0, yieldingthe Weiner Hopf tap weight solution given by EQN. (11):w _(opt) [n]=R _(uu) ⁻¹ [n]r _(du) [n]  (11)The MSE is generally a measure of the closeness of w to w_(opt). As afunction of the tap weight vector w, the MSE is then given by EQN. (12):J(w)=J _(min)+(w−w _(opt))^(H) R _(uu)(w−w _(opt))  (12)where

$\begin{matrix}{J_{\min} = {{\min\limits_{w}{J(w)}} = {{\sigma_{d}^{2} - {r_{du}^{H}R_{uu}^{- 1}r_{du}}} = {J\left( w_{opt} \right)}}}} & (12)\end{matrix}$In practice, for large N, inverting R_(uu) is prohibitively complicated.So a less complicated iterative solution is desirable. A steepestdescent method (“SD”) provides such a solution. It is given by EQN.(13):w[n+1]=w[n]−μ{∇J(w[n])}=w[n]−μ[R _(uu) [n]w[n]−r _(du) [n]]  (13)where μ is a step size parameter. However, estimating and updatingR_(uu) and r_(du) can also be complicated.

By using instantaneous approximations for R_(uu) and r_(du), EQN. (13)can be greatly simplified for practical applications. For example, asshown in EQN. (14) and EQN. (15),R _(uu) [n]≈u[n]u ^(H) [n]  (14)andr _(du) ≈u[n]d*[n],  (15)the gradient can be given by EQN. (16):

$\begin{matrix}\begin{matrix}{{\nabla{J\left( {w\lbrack n\rbrack} \right)}} = {2\left( {{{R_{uu}\lbrack n\rbrack}{w\lbrack n\rbrack}} - {r_{du}\lbrack n\rbrack}} \right)}} \\{\approx {{- 2}{{u\lbrack n\rbrack}\left\lbrack {{d^{*}\lbrack n\rbrack} - {{u^{H}\lbrack n\rbrack}{w\lbrack n\rbrack}}} \right\rbrack}}} \\{\approx {{- 2}{u\lbrack n\rbrack}{e^{*}\lbrack n\rbrack}}}\end{matrix} & (16)\end{matrix}$A practical LMS algorithm for the equalizer 520, as shown in EQN. (17),can then be determined from EQN. (13):

$\begin{matrix}\begin{matrix}{{w\left\lbrack {n + 1} \right\rbrack} = {{w\lbrack n\rbrack} - {\mu\left\{ {\nabla{J\left( {w\lbrack n\rbrack} \right)}} \right\}}}} \\{= {{w\lbrack n\rbrack} - {2\mu\;{u\lbrack n\rbrack}{e^{*}\lbrack n\rbrack}}}}\end{matrix} & (17)\end{matrix}$where μ is a step size parameter.

FIG. 6 shows a signal-to-noise-plus-interference-ratio (“SINR”) plot 600depicting a plurality of SINRs obtained from a plurality of symbolblocks with an LMS-based LE as discussed above. Similarly, FIG. 7 showsan MSE plot 700 depicting a plurality of MSEs obtained from theplurality of symbol blocks with the LMS-based LE as discussed above. Itis well known that SINR and MSE are related as shown in EQN. (18):

$\begin{matrix}{{SINR} = {10\log_{10}\frac{{signal}\mspace{14mu}{power}}{MSE}}} & (18)\end{matrix}$As shown in FIG. 6, time (in terms of symbol blocks processed by theequalizer 520) is measured along an x-axis 604, and SINR is measuredalong a y-axis 608. FIG. 6 shows an SINR curve 612 for different timesafter symbols are equalized with the equalizer 520. FIG. 6 shows thatthe curve 612 converges to an SINR value of about 15 dB after about3,000 symbol blocks have been equalized, where each block includes 512symbols. Similarly, as shown in FIG. 7, time is measured along an x-axis704, and MSE is measured along a y-axis 708. FIG. 7 shows an MSE curve712 for different times after symbols are equalized with the equalizer520. FIG. 7 shows that the MSE curve 712 converges to an MSE value ofabout 1 dB after about 3,000 symbol blocks have been equalized.

In general, equalizer convergence is achieved when the SINR rises abovea prescribed value before approaching a SINR convergence value such thatsubsequent error correction modules, such as the trellis decoder 528 andthe Reed-Solomon decoder 536, can nearly completely correct all dataerrors. For 8VSB, the prescribed value is about 15 dB, and the SINRconvergence value, which will depend on channel conditions, must belarger than that prescribed value. An example is shown in FIG. 6, wherevalues of SINR rise above 15 dB before approaching an SINR convergencevalue of about 16 dB.

FIG. 8 shows an LE system 800 that utilizes the LMS algorithm asdiscussed. The LE system 800 includes a linear transversal filter 804with tap weights w fed by an input data vector u[n]. The filter 804 hasan output y[n] that feeds a non-linear decision device 808. The decisiondevice 808 has an output d[n] that is a set of likely symbolstransmitted. The output d[n] is subtracted from the output y[n] tocreate an error signal e[n]. The error signal e[n] is used by an LMSalgorithm 812 to update the tap weights w of the filter 804 for timen+1.

SUMMARY

The following summary sets forth certain exemplary embodiments of theinvention. It does not set forth all embodiments of the invention andshould in no way be construed as limiting of embodiments of theinvention.

In one embodiment, the invention includes a method of determining a stepsize of an adaptive equalizer for a digital data receiver. The datareceived by the receiver includes coded symbols and uncoded symbols. Themethod includes determining a first error estimate based on decodedsymbols corresponding to the coded symbols, determining a second errorestimate based on the uncoded symbols, adaptively selecting the firsterror estimate or the second error estimate based on a convergencecriterion, and determining a step size based on the selected errorestimate.

In another embodiment, the invention includes a method of determining astep size of an adaptive equalizer for a digital data receiver. The datareceived by the receiver includes coded symbols and uncoded symbols. Themethod includes, based on a convergence criterion, selecting a firstsignal estimation process or a second signal estimation process, thefirst signal estimation process utilizing decoded symbols correspondingto the coded symbols, and the second signal estimation process utilizingthe uncoded symbols. The method also includes determining a signalestimate based on the selected signal estimation process, determining anerror estimate based on the received data and the signal estimate, anddetermining a step size based on the error estimate.

In another embodiment, the invention includes an adaptive equalizer fora digital data receiver. The data received by the receiver includescoded symbols and uncoded symbols. The equalizer includes a selectionmodule, an error estimator, and a step size generator. The selectionmodule is configured to select, based on a convergence criterion,decoded symbols or the uncoded symbols, the decoded symbolscorresponding to the coded symbols. The error estimator is configured tocompare the received data and the selected symbols, and to generate anerror estimate based on the comparison. The step size generator isconfigured to generate a step size based on the error estimate.

In another embodiment, the invention includes a device configured toprocess digital television signals. The device includes a receiver thatincludes a demodulator, a decoder, a slicer, and an equalizer. Thereceiver is configured to receive radio frequency signals modulated withdata including coded symbols and uncoded symbols. The demodulator isconfigured to demodulate the received radio frequency signals to producethe coded symbols and the uncoded symbols. The decoder is configured todecode the coded symbols to produce corresponding decoded symbols, andthe slicer is configured to slice the uncoded symbols to producecorresponding sliced symbols. The equalizer includes a selection module,an error estimator, and a step size generator. The selection module isconfigured to select, based on a convergence criterion, the decodedsymbols or the sliced symbols. The error estimator is configured tocompare the data and the selected symbols, and to generate an errorestimate based on the comparison. The step size generator is configuredto generate a step size based on the error estimate.

Other aspects of the invention will become apparent by consideration ofthe detailed description and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an exemplary 8VSB data frame.

FIG. 2 shows an exemplary field sync segment.

FIG. 3 shows an exemplary data segment.

FIG. 4 shows an exemplary digital data transmitter.

FIG. 5 shows an exemplary digital data receiver.

FIG. 6 shows a signal-to-noise-plus-interference-ratio (“SINR”) plotdepicting a plurality of SINRs obtained from a plurality of symbolblocks with the equalizer of FIG. 5.

FIG. 7 shows a mean-squared-error (“MSE”) plot depicting a plurality ofMSEs obtained from a plurality of symbol blocks with the LMS-basedlinear equalizer of FIG. 5.

FIG. 8 shows a typical linear equalizer system having a decision device.

FIG. 9A shows a digital communications device according to an embodimentof the invention.

FIG. 9B shows a method according to an embodiment of the invention.

FIG. 9C shows a linear equalizer according to an embodiment of theinvention.

FIG. 10 shows an MSE estimate plot based on coded symbols.

FIG. 11 shows an estimate plot based on uncoded symbols.

FIG. 12 shows an estimate plot showing a portion of the plot of FIG. 10.

FIG. 13 shows an estimate plot showing a portion of the plot of FIG. 11.

FIG. 14 shows an estimate plot based on selective use of coded anduncoded symbols.

FIG. 15 shows an estimate plot showing a portion of the plot of FIG. 14.

FIG. 16 shows a linear equalizer system according to an embodiment ofthe invention.

DETAILED DESCRIPTION

Before any embodiments of the invention are explained in detail, it isto be understood that the invention is not limited in its application tothe details of construction and the arrangement of components set forthin the following description or illustrated in the following drawings.The invention is capable of other embodiments and of being practiced orof being carried out in various ways. Also, it is to be understood thatthe phraseology and terminology used herein are for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having” and variations thereof herein ismeant to encompass the items listed thereafter and equivalents thereofas well as additional items.

As should also be apparent to one of ordinary skill in the art, thesystems shown in the figures are models of what actual systems might belike. Many of the modules and logical structures described are capableof being implemented in software executed by a microprocessor or asimilar device or of being implemented in hardware using a variety ofcomponents including, for example, application specific integratedcircuits (“ASICs”). Terms like “equalizer” or “decoder” may include orrefer to both hardware and/or software. Furthermore, throughout thespecification capitalized terms are used. Such terms are used to conformto common practices and to help correlate the description with thecoding examples, equations, and/or drawings. However, no specificmeaning is implied or should be inferred simply due to the use ofcapitalization. Thus, the claims should not be limited to the specificexamples or terminology or to any specific hardware or softwareimplementation or combination of software or hardware.

As noted above, the step size μ of EQN. (17) controls a rate at whichthe LMS adaptive equalizer tap weights w converge to near an optimumw_(opt). It is desirable to use a larger step size to decrease theamount of time needed until convergence is obtained. However, a largerstep size leads to a larger steady state MSE, or lower SINR, at theoutput of an equalizer after convergence. Hence, after the equalizer isclose to convergence, a smaller step size is desirable. Therefore, it isgenerally advantageous to have a variable step size whose value dependson a “closeness” of w to w_(opt), thereby enabling a receiver to use alarger step size while the adaptive filter is converging and a smallerstep size after convergence.

The ability of an adaptive equalizer to track a nonstationary channel isalso a concern in appropriately choosing a step size μ. If the equalizerconverges close to the optimal tap weight vector w_(opt) and is runningwith a small step size, but then the channel conditions change, theequalizer must adequately track and adapt the tap weight vector w.Detection of changes in channel conditions and a switch to a larger stepsize μ, even though the equalizer has previously converged, isadvantageous in this situation.

Embodiments of the invention include methods, systems, and devices foradaptively selecting a step size of an equalizer. In one specificembodiment, an adaptive equalizer selectively uses coded symbols oruncoded symbols to determine a step size by which to update tap weightsused by a transversal filter. Selective use of coded symbols or uncodedsymbols can enable a more accurate estimation of error throughoutvarious states of the equalizer, which estimation in turn can beemployed to determine an appropriate step size. For instance, uncodedsymbols may be employed before a predetermined convergence state of theequalizer, and coded symbols may be employed once the predeterminedconvergence state is reached.

Embodiments herein can achieve improved performance than that achievedin existing digital communication receivers. For instance, embodimentsherein can be employed to respond adaptively to changing channelconditions and more effectively select an appropriate step size. In oneembodiment, iterative processes are employed to detect when current tapweights are no longer sufficiently close to optimal tap weights, and tomodify the step size based on coded symbols or uncoded symbols asappropriate, so as to move closer to the optimal tap weight solution.

Although some embodiments herein focus on processing (e.g., reception)of digital television signals, the invention may be implemented inconnection with other kinds of digital signals. Similarly, although someembodiments herein relate to the 8VSB RF modulation format, theinvention may be implemented in connection with other modulationformats, such as formats that include coded information and a prioriknown information.

Additionally, although some embodiments herein relate to linearequalizers (“LEs”), the invention may be implemented in connection withother equalizer architectures, such as, for example, decision feedbackequalizers (“DFEs”) and predictive decision feedback equalizers(“pDFEs”).

FIG. 9A shows a digital communications device 950 according to anembodiment of the invention. The device 950 can be implemented as, or inconjunction with, any of a host of devices, such as, for example, areceiver (e.g., digital communication receiver), tuner, PC adapter card,set top box, DVD recorder, HDTV recorder, television, phone, or handhelddevice. The device 950 can be implemented partially or entirely on asemiconductor (e.g., FPGA semiconductor) chip, such as a chip developedthrough a register transfer level (RTL) design process. The device 950includes a receiver module 955 and optional hardware and/or softwaremodule(s) 990 that provide additional functions (e.g., displayfunctions). In other embodiments, the device 950 includes more or fewermodules than those depicted. For example, certain of the depictedmodules can be implemented on other devices that interface with thedevice 950 (e.g., the receiver module 955 can communicate with a displaymodule incorporated in a separate device).

The receiver module 955 includes a demodulator 960, a decoder 965, andan equalizer 970. In some embodiments, the receiver module 955 includesone or more additional modules, such as, for example, a tuner, a syncand timing recovery module, a matched filter, a phase tracker, adeinterleaver, a second decoder, a slicer, and/or a derandomizer. Theequalizer 970 includes a selection module 975, an error estimator 980,and a step size generator 985. Exemplary implementations of theequalizer 970 are described in further detail below.

FIG. 9B shows a method 991 according to an embodiment of the invention.The method 991 can be employed by, or in conjunction with, an equalizerfor a digital data receiver in order to determine a step size of theequalizer. For instance, the method 991 can be employed by the receivermodule 955 and/or equalizer 970 of FIG. 9A, as well as in connectionwith other embodiments described below. In task 993, data is receivedthat includes coded symbols and uncoded (e.g., a priori known) symbols.In task 994, a first error estimate is determined based on decodedsymbols corresponding to the coded symbols. In task 995, a second errorestimate is determined based on the uncoded symbols. In task 996, thefirst error estimate or the second error estimate is adaptively selectedbased on a convergence criterion. In task 997, a step size is determinedbased on the selected error estimate. The method 991 can be iterativelyperformed as a datastream is received and processed.

Variations of the method 991 are within the scope of embodiments of theinvention. For instance, in one embodiment, a method selects eitherdecoded symbols or uncoded symbols based on a convergence criterion;determines a signal estimate based on the selected symbols; determinesan error estimate based on received data and the signal estimate; anddetermines a step size based on the error estimate.

FIG. 9C shows a linear equalizer (“LE”) 900 according to an embodimentof the invention that can be implemented in a digital receiver. Theequalizer 900 includes a linear transversal filter 904 having tapweights w and an output y[n], a selection module 908, an error estimator912, a step size generator 916 that generates a step size parameterμ[k], and an LMS tap weight module 920 that adjusts the tap weights wbased on the step size parameter μ[k]. In some embodiments, the errorestimator 912 includes an MSE estimator. In such cases, the step sizegenerator 916 generates the step size parameter μ[k] based on errorinformation provided by the MSE estimator. Although FIG. 9C shows an LE,other types of equalizers, such as DFEs and pDFEs, can be employed.

As previously noted, 8VSB signals include a combination of 8-leveltrellis coded symbols and uncoded 2-level symbols. The selection module908 generates an output d[n], which in turn is subtracted from y[n] toobtain e[n] at a summing node 924. The selection module 908 also feedsthe output d[n] to the error estimator 912. The selection module 908includes a trellis decoder 928 that decodes the coded symbols at theequalizer output y[n] using the Viterbi algorithm. In the embodimentshown, the decoder 928 has a zero delay or a traceback depth of oneoutput. While longer traceback depth decisions are generally morereliable, they incur a longer delay, which can be unacceptable if aninstantaneous e[n] is needed for the LMS update.

Most of the uncoded symbols including all segment sync symbols and thefirst 728 symbols of the field sync segment are a priori known. Theseare perfectly “decoded” by reading them out of a memory at appropriatetimes. The 92 unknown 2-level symbols at the end of the field syncsegment are decoded by slicing at a midpoint with a slicer 932 since theunknown symbols are 2-level symbols.

Through a control line 940, a synchronizing control signal indicates tothe selection module 908 and a switch 936 which type of symbol is beingdecoded. Exemplary methods for deriving this control signal firstrequire symbol clock recovery, then data field synchronization, both ofwhich occur in the preceding sync and timing recovery block, and then amodulo 260,416 symbol clock rate counter feeding a comparator thatactivates the control line 940 according to the type of symbol.

The error estimator 912 controls the adjustable step size parameter μ[k]that is being fed to the LMS module 920. In one embodiment, the errorestimator 912 includes a high MSE indicator and a low MSE indicator. Ahigh MSE (low SINR) indicates that w is not close to w_(opt), and thus aneed for a larger step size. A low MSE (high SINR) indicates that w isclose to w_(opt), and thus a desirability of a smaller step size. Anexemplary method of MSE estimation (or, equivalently, SINR estimation)for the 8VSB signal is discussed below.

In some embodiments, the error estimator 912 periodically estimates anerror, such as an MSE, every block of M symbols times from the trellisdecoded symbols outputted by the decoder 928 as follows. For example, inthe case of an MSE estimate, an instantaneous MSE estimate at block timek is given by EQN. (19).

$\begin{matrix}{{\xi_{dec}^{2}\lbrack k\rbrack} = {\left( \frac{1}{M} \right){\sum\limits_{i = 0}^{M - 1}\;\left( {{y\left\lbrack {m + i} \right\rbrack} - {d\left\lbrack {m + i} \right\rbrack}} \right)^{2}}}} & (19)\end{matrix}$where k is a block index, symbol index base m=(k−1)M, y is the equalizeroutput, d is the zero delay output of the trellis decoder 928, and M isa selected block size. Similarly, in the case of an MSE estimate, anaveraged MSE estimate is given by EQN. (20).ξ_(dec,β) ² [k]=(1−β_(dec))ξ_(dec) ² [k]+β _(dec)ξ_(dec,β) ²[k−1],0<β_(dec)<1  (20)Note that values of β_(dec) are typically close to 1, with a smallervalue providing a noisier MSE estimate but faster tracking of a changingMSE.

FIG. 10 shows an estimate plot 1000. Times (in terms of symbol blocks atthe output of the equalizer 900) are measured along an x-axis 1004, andvalues of the MSE are measured along a y-axis 1008. A curve 1012 showsactual MSEs obtained, and a curve 1018 shows values of the MSE estimateξ_(dec,β) ². FIG. 10 shows that when the actual MSEs (curve 1012) areabove about 0.9 (equivalent to an SINR below 13.75 dB), the estimatedMSE ξ_(dec,β) ² is considerably below the actual MSE.

Alternatively, error values may be estimated using only the a prioriknown 2-level segment sync symbols. For example, in the case of an MSEestimate, whenever d[p] . . . d[p+3] are segment sync symbols, then aninstantaneous MSE estimate at segment j is given by EQN. (21).

$\begin{matrix}{{\xi_{seg}^{2}\lbrack j\rbrack} = {\left( \frac{1}{4} \right){\sum\limits_{i = 0}^{3}\;\left( {{y\left\lbrack {p + i} \right\rbrack} - {d\left\lbrack {p + i} \right\rbrack}} \right)^{2}}}} & (21)\end{matrix}$where j is a segment index, and p=832(j−1) is a symbol index (note thatblocks k and segments j are in general asynchronous). Similarly, in thecase of an MSE estimate, whenever d[p] . . . d[p+3] are segment syncsymbols, then an averaged MSE estimate is given by EQN. (22).ξ_(seg,β) ² [j]=(1−β_(seg))ξ_(seg) ² [j]+β _(seg)ξ_(seg,β) ²[j−1],0<β_(seg)<1  (22)Note that values of β_(seg) are typically close to 1, with a smallervalue providing a noisier MSE estimate but faster tracking of a changingMSE.

FIG. 11 shows an estimate plot 1100. Times (in terms of symbol blocks atthe output of the equalizer 900) are measured along an x-axis 1104, andvalues of the MSE are measured along a y-axis 1108. A curve 1112 showsactual MSEs obtained, and a curve 1118 shows values of the MSE estimateξ_(seg,β) ². FIG. 11 shows that values of ξ_(seg,β) ² are more accuratethan ξ_(dec,β) ² at high MSE (low SINR).

FIG. 12 shows an estimate plot 1200 showing a portion of the plot 1000of FIG. 10 between symbol blocks 2600 and 3800. Times (in terms ofsymbol blocks at the output of the equalizer 900) are measured along anx-axis 1204, and values of the MSE are measured along a y-axis 1208. Acurve 1212 shows actual MSEs obtained, and a curve 1218 shows values ofthe MSE estimate ξ_(dec,β) ². Similarly, FIG. 13 shows an estimate plot1300 showing a portion of the plot 1100 of FIG. 11 between symbol blocks2600 and 3800. Times (in terms of symbol blocks at the output of theequalizer 900) are measured along an x-axis 1304, and values of the MSEare measured along a y-axis 1308. A curve 1312 shows actual MSEsobtained, and a curve 1318 shows values of the MSE estimate ξ_(dec,β) ².

As shown in FIG. 12 and FIG. 13, while the ξ_(seg,β) ² estimate isbetter than ξ_(dec,β) ² at high MSE, the ξ_(seg,β) ² estimate is noisierthan ξ_(dec,β) ² as MSE decreases because ξ_(seg) ² is averaged overfewer symbols than is ξ_(dec) ² (4<<M). Thus, ξ_(seg) ² represents anoisier estimate of the actual MSE. As such, each of the two methodsprovides relatively better estimates for respective MSE regions. Hence,according to various embodiments of the invention, these methods areselectively combined to provide a more accurate MSE estimate both at lowand high MSE.

For example, in one embodiment involving EQN. (23) and EQN. (24), ifξ_(dec,β) ² is less than a predetermined MSE value (e.g., associatedwith a convergence state), the step size generator 916 selects ξ_(dec,β)² as the error estimate EstMSE. If ξ_(dec,β) ² is greater than or equalto the predetermined MSE value, the step size generator 916 selectsξ_(seg,β) ² as the error estimate EstMSE. In some embodiments, thepredetermined MSE value is about 0.9. That is,

-   -   If ξ_(dec,β) ²[k]<0.9 (equivalent to SINR>13.75 dB)        EstMSE[k]=ξ _(dec,β) ² [k] (near or post convergence)  (23)    -   Else        EstMSE[k]=ξ _(seg,β) ² [j] (pre convergence)  (24)

FIG. 14 shows an error estimate plot 1400 based on selectively combininguse of coded and uncoded symbols. Times (in terms of symbol blocks atthe output of the equalizer 900) are measured along an x-axis 1404, andvalues of the MSE in dB are measured along a y-axis 1408. A curve 1412shows actual MSEs obtained, and a curve 1416 shows values of the MSEestimate EstMSE determined by EQNS. (21)-(24) and associated logic. FIG.15 similarly shows a portion 1400′ of the estimate plot 1400 from symbolblocks 2600 to 3800. The portion 1400′ shows the error estimator 912transitioning from use of uncoded symbols (i.e., ξ_(seg,β) ²) to use ofcoded symbols (i.e., ξ_(dec,β) ²) at a switch point 1420. In theembodiment shown, the switch point is set at about MSE=0.9.

Once the error estimator 912 has determined an error estimate, the stepsize generator 916 uses the error estimate EstMSE[k] to select avariable LMS step size depending on a range within which the errorestimate EstMSE[k] falls. For example, as shown in expression (25), ifthe error estimate EstMSE[k] falls within a range

_(r), the step size generator 916 sets the step size parameter μ[k]equal to a predetermined step size μ_(r).

-   -   If EstMSE[k]ε        _(r)        μ[k]=μ_(r)  (25)    -   end        In one embodiment, smaller values of EstMSE correspond to lower        values of μ_(r). For example, the step size generator 916 can        use three EstMSE[k] ranges: a first range        ₁, a second range        ₂, and a third range        ₃. If EstMSE[k] is within the first range        ₁, which is less than or equal to 0.7 dB (equivalent to an        estimated SINR of about 14.75 dB), the step size generator 916        uses a step size of μ₁. Similarly, if EstMSE[k] is within the        second range        ₂, which is between 0.7 dB and 0.9 dB (equivalent to an        estimated SINR of about 13.75 dB), the step size generator 916        uses a step size of μ₂. If EstMSE[k] is within the third range        ₃, which is above 0.9 dB, the step size generator 916 uses a        step size of μ₃. In such cases, μ₁ is the smallest among the        three step sizes, and μ₃ is the largest among the three step        sizes.

In other embodiments, the step size generator 916 determines the stepsize differently. For instance, the step size generator 916 may employ afunction (e.g., a continuous function) that computes the step size basedon information received from the error estimator 912. In someembodiments, the step size is given by EQN. (25′).μ[k]=γEstMSE[k]  (25′)where γ is a predetermined positive real constant.

FIG. 16 shows a second LE system 1600 according to an embodiment of theinvention. The LE system 1600 is an alternative embodiment of the LEsystem 900 of FIG. 9C. Similar to the LE system 900, the LE system 1600includes a linear transversal filter 1604 having tap weights w and anoutput y[n], a selection module 1608, an error estimator 1612, a stepsize generator 1616 that generates a step size parameter μ[k], and anLMS tap weight module 1620 that adjusts the tap weights w based on thestep size parameter μ[k]. Furthermore, the selection module 1608includes a decoder 1621 that decodes the coded symbols at the equalizeroutput y[n] using the Viterbi algorithm, and a slicer 1622 that slicesthe unknown 2-level symbols at a midpoint.

The LE system 1600 also includes a plurality of delay blocks 1624, 1628,1632 that introduce delays during signal processing. In someembodiments, the error estimator 1612 includes an MSE estimator. In suchcases, the step size generator 1616 generates the step size parameterμ[k] based on errors estimated by the MSE estimator. However, unlike theLE system 900 of FIG. 9C, the LE system 1600 uses a trellis decoder 1621with a full traceback depth D+1 (which thus has a decode delay D) anddelay blocks 1624, 1628, 1632, all with delay D. A full traceback depthof the trellis decoder 1621 can generate an output that has a higherreliability than a zero delay output generated by the trellis decoder928 of FIG. 9C. As such, the trellis decoder 1621 with adjustabletraceback depth controlled by the delay blocks 1624, 1628, 1632 canprovide a more accurate ξ_(dec,β) ² value if both the a priori known2-level symbol output memory and the y[n] signal are likewise delayed byD. Accordingly, the input to the error estimator 1612 is d_(D)[n], whichis d_(D)[n] delayed by an amount D.

In the LE system 1600, for the coded symbols, an instantaneous MSEestimate at block time k given by EQN. (26).

$\begin{matrix}{{\xi_{dec}^{2}\lbrack k\rbrack} = {\left( \frac{1}{M} \right){\sum\limits_{i = 0}^{M - 1}\;\left( {{y_{D}\left\lbrack {m + i} \right\rbrack} - {d_{D}\left\lbrack {m + i} \right\rbrack}} \right)^{2}}}} & (26)\end{matrix}$where k is a block index, the symbol index base m=(k−1)M, y_(D) is adelayed equalizer output, d is a full traceback output of the trellisdecoder 1621 with a delay D, and M is a selected block size. Similarly,in the LE system 1600, for the uncoded symbols, an instantaneous MSEestimate at segment j is given by EQN. (27).

$\begin{matrix}{{\xi_{seg}^{2}\lbrack j\rbrack} = {\left( \frac{1}{4} \right){\sum\limits_{i = 0}^{3}\;\left( {{y_{D}\left\lbrack {p + i} \right\rbrack} - {d_{D}\left\lbrack {p + i} \right\rbrack}} \right)^{2}}}} & (27)\end{matrix}$where j is a segment index, and p=832(j−1) is a symbol index. Blocks kand segments j are in general asynchronous.

It should be noted that the numerical values described above andillustrated in the drawings are exemplary values only. Other numericalvalues can also be used.

Various convergence criteria may be used in connection with embodimentsof the invention. In some embodiments, an initial error (e.g., MSE)estimate based on coded symbols is used to determine if coded symbolsshould continue to be used for further error estimates, or if uncodedsymbols should be used for further error estimates. Other exemplaryembodiments use convergence criteria based on how often the sign of anerror gradient changes.

Various features and advantages of the invention are set forth in thefollowing claims.

1. A method of determining a step size of an adaptive equalizer for adigital data receiver, data received by the receiver including codedsymbols and uncoded symbols, the method comprising: determining a firsterror estimate based on decoded symbols corresponding to the codedsymbols; determining a second error estimate based on the uncodedsymbols; comparing the first error estimate to an error estimatethreshold value; continuously adaptively selecting one of the determinedfirst error estimate and the determined second error estimate based onthe comparison to the error estimate threshold value, wherein, when thefirst error estimate is less than the error estimate threshold value,the first error estimate is selected, and when the first error estimateis greater than the error estimate threshold value, the second errorestimate is selected, and wherein, when no uncoded symbols are availableto determine the second error estimate, a previously calculated valuefor the second error estimate is used for the adaptive selection of oneof the determined first error estimate and the determined second errorestimate; and determining a step size based on the selected errorestimate.
 2. The method of claim 1, wherein the coded symbols aretrellis-coded 8-level symbols.
 3. The method of claim 1, furthercomprising decoding the coded symbols to produce the decoded symbols. 4.The method of claim 3, wherein the coded symbols are decoded with a zerodelay.
 5. The method of claim 1, wherein the first error estimate is amean-squared-error estimate.
 6. The method of claim 5, wherein the firsterror estimate is an averaged mean-squared-error estimate determinedbased on a plurality of instantaneous mean-squared-error estimates. 7.The method of claim 1, wherein the uncoded symbols comprise 2-levelsegment sync symbols, the method further comprising slicing the 2-levelsymbols.
 8. The method of claim 7, wherein the second error estimate isdetermined based on the sliced 2-level symbols.
 9. The method of claim1, wherein determining a step size based on the selected error estimatecomprises: comparing the selected error estimate with a plurality oferror ranges including a first error range, each of the error rangeshaving a corresponding step size; and selecting the corresponding stepsize of the first error range if the selected error estimate is withinthe first error range.
 10. The method of claim 1, wherein the errorestimate threshold value comprises one of a predeterminedmean-squared-error and a predetermined signal to noise plus interferenceratio, and wherein the second error estimate is selected when the firsterror estimate is equal to the error estimate theshold value.
 11. Themethod of claim 1, wherein the equalizer is a linear-mean-squared (LMS)equalizer for 8-level-vestigial-sideband (8VSB) modulated signals. 12.The method of claim 1, wherein the method is iteratively performed. 13.The method of claim 1, wherein the digital data receiver is a digitaltelevision receiver.
 14. A method of determining a step size of anadaptive equalizer for a digital data receiver, data received by thereceiver including coded symbols and uncoded symbols, the methodcomprising: based on a convergence criterion, selecting one of a firstsignal estimation process and a second signal estimation process, thefirst signal estimation process utilizing decoded symbols correspondingto the coded symbols, and the second signal estimation process utilizingthe uncoded symbols; determining a signal estimate based on the selectedsignal estimation process; determining an error estimate based on thereceived data and the signal estimate; and determining a step size basedon the error estimate by comparing the error estimate with a pluralityof error ranges including a first error range, each of the error rangeshaving a corresponding step size, and selecting the corresponding stepsize of the first error range if the error estimate is within the firsterror range.
 15. The method of claim 14, wherein the convergencecriterion is indicative of a signal to noise plus interference ratio.16. The method of claim 14, wherein the decoded symbols are trellisdecoded symbols, and the uncoded symbols are segment sync symbols. 17.The method of claim 14, wherein the error estimate is amean-squared-error estimate.
 18. The method of claim 14, wherein thedigital data receiver is a digital television receiver.
 19. An adaptiveequalizer for a digital data receiver, data received by the receiverincluding coded symbols and uncoded symbols, the equalizer comprising: aselection module configured to continually select, based on aconvergence criterion, one of decoded symbols and the uncoded symbols,the decoded symbols corresponding to the coded symbols; an errorestimator configured to compare the received data and the selectedsymbols, and to generate an error estimate based on the comparison; anda step size generator configured to generate a step size based on theerror estimate, wherein the step size generator comprises a comparatorconfigured to compare the error estimate with a plurality of errorranges including a first error range, each of the error ranges having acorresponding step size, and to select the corresponding step size ofthe first error range if the error estimate is within the first errorrange.
 20. The equalizer of claim 19, wherein the equalizer isimplemented on a semiconductor chip.
 21. The equalizer of claim 19,wherein the coded symbols are trellis-coded symbols, further comprisinga trellis decoder configured to decode the coded symbols to produce thedecoded symbols.
 22. The equalizer of claim 19, wherein the errorestimate comprises a mean-squared-error estimate.
 23. The equalizer ofclaim 19, wherein the uncoded symbols comprise segment sync symbols, theequalizer further comprising a slicer configured to slice the segmentsync symbols to produce sliced uncoded symbols.
 24. The equalizer ofclaim 23, wherein the selection module is configured to select, based onthe convergence criterion, one of the decoded symbols and the sliceduncoded symbols.
 25. The equalizer of claim 19, wherein the convergencecriterion comprises one of a predetermined mean-squared-error and apredetermined signal to noise plus interference ratio.
 26. The equalizerof claim 19, wherein the equalizer is implemented in a digitaltelevision receiver.
 27. A device configured to process digitaltelevision signals, the device comprising: a receiver including ademodulator, a decoder, a slicer, and an equalizer, the receiverconfigured to receive radio frequency signals modulated with dataincluding coded symbols and uncoded symbols, the demodulator configuredto demodulate the received radio frequency signals to produce the codedsymbols and the uncoded symbols, the decoder configured to decode thecoded symbols to produce corresponding decoded symbols, the slicerconfigured to slice the uncoded symbols to produce corresponding slicedsymbols, and the equalizer including a selection module configured toselect, based on a convergence criterion, one of the decoded symbols andthe sliced symbols, an error estimator configured to compare the dataand the selected symbols, and to generate an error estimate based on thecomparison, and a step size generator configured to generate a step sizebased on the error estimate.
 28. The device of claim 27, wherein thedevice is a tuner, a television, a PC adapter card, a set top box, a DVDrecorder, a HDTV recorder, a phone, or a handheld device.
 29. A methodof determining a step size for a linear-mean-squared (LMS) equalizer of8-level-vestigial-sideband (8VSB) modulated signals having trellis-codedsymbols and segment sync symbols, the method comprising: (a) decodingthe trellis-coded symbols; (b) determining a first mean-squared-errorestimate based on the decoded trellis-coded symbols; (c) slicing thesegment sync symbols; (d) determining a second mean-squared-errorestimate based on the sliced segment sync symbols; (e) comparing thefirst mean-squared-error estimate to an error estimate threshold value;(f) continually adaptively selecting one of the first mean-squared-errorestimate and the second mean-squared-error estimate based on thecomparison to the error estimate threshold value, wherein, when thefirst mean-squared-error estimate is less than the error estimatethreshold value, the first mean-squared-error estimate is selected, andwhen the first mean-squared-error estimate is greater than or equal tothe error estimate threshold value, the second mean-squared-errorestimate is selected, and wherein, when no uncoded symbols are availableto determine the second mean-squared-error estimate, a previouslycalculated value for the second mean-squared-error estimate is used forthe adaptive selection of one of the determined first mean-squared-errorestimate and the determined second mean-squared-error estimate; (g)determining a step size based on the selected error estimate; and (h)iteratively performing acts (a)-(g).
 30. A digital communicationreceiver configured to receive radio frequency signals modulated withdata including coded symbols and a priori known uncoded symbols, thereceiver comprising: a demodulator configured to demodulate the receivedradio frequency signals to produce the coded symbols and the a prioriknown uncoded symbols; a decoder configured to decode the coded symbolsto produce corresponding decoded symbols; and an equalizer including aselection module configured to select, based on a convergence criterion,one of the decoded symbols and the a priori known uncoded symbols, anerror estimator configured to compare the data and the selected symbols,and to generate an error estimate based on the comparison, and a stepsize generator configured to generate a step size based on the errorestimate.